The goal of the Training Core (Pis Randy Gollub, MGH and Sona Pujol, BWH) is to lower barriers to effective communication between the clinical translational investigators and the software engineers engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. The Training Core addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. We began this task six years ago by focusing on the most effective means for our own internal NA-MIC team to communicate. First we identified critical domains of knowledge that were necessary for all to comprehend. Then we developed an effective means for rapidly bringing everyone to a high level of competence in these domains. For instance, during our first Project Event (Programmer's Week 2004), we educated the image algorithm developers interested in diffusion tractography about functional and structural neuroanatomy, focusing explicltiy on the major fiber tracts. During all large group meetings we made it a policy to pause to define unfamiliar or potentially ambiguous terms until we had arrived at a common vocabulary. The continuing evolution from that initial gathering to our most recent Project Events is clear testament to the success ofthis approach. Although packaged as a training exercise for the NA-MIC Kit, the primary activify of the Training Core in the first NA-MIC funding cycle was to develop and deliver hands-on experiential learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. Thus, we used components ofthe NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. All of our tutorials can be self-taught or administered by an instmctor. Each tutorial follows the rubric established in How People Learn (1, 2,3), which requires learner-centered, goal-oriented experiential teaching. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, i.e., clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. On the basis of positive feedback from attendees of past workshops, we believe that facilitating the adoption ofthe NA-MIC Kit for education, research, and medical image analysis software development will have a tremendous positive impact on clinical translational medicine.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brigham and Women's Hospital
United States
Zip Code
Liu, Sidong; Cai, Weidong; Pujol, Sonia et al. (2016) Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci 8:23
Kapur, Tina; Pieper, Steve; Fedorov, Andriy et al. (2016) Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience. Med Image Anal 33:176-80
Luo, Xinchao; Zhu, Lixing; Zhu, Hongtu (2016) Single-index varying coefficient model for functional responses. Biometrics 72:1275-1284
Roth, Robert M; Garlinghouse, Matthew A; Flashman, Laura A et al. (2016) Apathy Is Associated With Ventral Striatum Volume in Schizophrenia Spectrum Disorder. J Neuropsychiatry Clin Neurosci 28:191-4
Herrick, Rick; Horton, William; Olsen, Timothy et al. (2016) XNAT Central: Open sourcing imaging research data. Neuroimage 124:1093-6
Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2016) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp :
Wang, Bo; Prastawa, Marcel; Irimia, Andrei et al. (2016) Modeling 4D Pathological Changes by Leveraging Normative Models. Comput Vis Image Underst 151:3-13
Li, Bolun; Shi, Jie; Gutman, Boris A et al. (2016) Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 11:e0152901
Ruellas, Antonio Carlos de Oliveira; Yatabe, Marilia Sayako; Souki, Bernardo Quiroga et al. (2016) 3D Mandibular Superimposition: Comparison of Regions of Reference for Voxel-Based Registration. PLoS One 11:e0157625
Oguz, Ipek; Cates, Josh; Datar, Manasi et al. (2016) Entropy-based particle correspondence for shape populations. Int J Comput Assist Radiol Surg 11:1221-32

Showing the most recent 10 out of 625 publications